import torch
from torch.utils.data import DataLoader
# 假设你有自定义的数据集类ColQwen2Dataset
# from your_dataset_module import ColQwen2Dataset
from colpali.colpali_engine import ColQwen2Model

# === 配置参数 ===
MODEL_NAME = "your_pretrained_model"
SAVE_PATH = "models/colqwen2_trained"
BATCH_SIZE = 8
NUM_EPOCHS = 3
LR = 1e-4

# === 数据加载 ===
# train_dataset = ColQwen2Dataset("data/train/")
# train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
# 这里只做伪代码示例
train_loader = []  # TODO: 替换为实际DataLoader

# === 模型初始化 ===
model = ColQwen2Model.from_pretrained(MODEL_NAME)
model.train()
optimizer = torch.optim.AdamW(model.parameters(), lr=LR)

# === 训练循环 ===
for epoch in range(NUM_EPOCHS):
    for batch in train_loader:
        # 假设batch包含input和label
        # inputs, labels = batch
        # outputs = model(**inputs)
        # loss = ... # 计算损失
        # loss.backward()
        # optimizer.step()
        # optimizer.zero_grad()
        pass  # TODO: 替换为实际训练逻辑
    print(f"Epoch {epoch+1} finished.")

# === 保存模型 ===
model.save_pretrained(SAVE_PATH)
print(f"模型已保存到 {SAVE_PATH}") 